February 2018
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previous generations. As a result it can pro-
vide the higher levels of performance needed
by edge computing devices that are constantly
on, always ready to instantly process com-
mands locally without going to the cloud.
There is support for functions such as gesture
detection, facial recognition, audio enhance-
ment, audio beam forming, phrase detection,
double tap, shake-to-wake and pedestrian
dead reckoning (PDR). As well as 1.1 Mbits
of SRAM and 8 DSP blocks, the FPGAs in
this family incorporate up to 5K look-up
tables (LUTs) and non-volatile configura-
tion memory (NVCM) for instant-on appli-
cations. With under 100 µW used in standby
and compact QFN packages, they are highly
suited to deployment in space-constrained
environments with power limitations. Key
applications include always-on sensor buffers
and distributed processing for mobile devices
at sub-1mW power consumption, always-on
sensor functionality while the AP is in sleep
mode, etc.
But edge computing is not just about more
powerful hardware. FogHornSystems for
example has developed a platform that it says
can provide real-time analytics on ultra-small
footprint edge devices. This allows develop-
ers to get data from IoT applications, reduc-
ing bandwidth usage and cost. It minimizes
latency and increases reliability, as well as
providing real-time responsiveness.
The embeddable software Lightning Micro
of the company has a small memory foot-
print (less than 256 MBytes) for data pro-
cessing and real-time analytics at the edge
using a C++ SDK. The data is fed in via
IoT protocols, such as OPC-UA, MQTT
and Modbus, and the real-time streaming
analytic engine can be configured through
an easy-to-use expression language and
hundreds of built-in functions. Greenwave
Systems is also looking at how analytics can
be implemented at the edge of the network.
It has teamed up with Wind River to port its
AXON Predict analytics engine to VxWorks
- allowing customised analytics that boost
computational power and real-time intelli-
gence in industrial IoT designs.
To give VxWorks developers a tool to analyse
and autonomously respond to high-volume
streaming sensor data at the source, AXON
Predict will provide developers with embed-
ded analytics that learn patterns, provide
insights and take actions inside connected
device operations and behaviours. This edge
analytics engine allows developers to build a
set-and-forget application with intelligence
and process critical data at the edge of a net-
work in real-time. This enables machines and
smart sensors to collect information at every
step of the network, automatically detect
Figure 1. Moving processing to the edge of the network is becoming essential for IoT.
(Source: EdgeX Foundry)
Figure 2. iCE40 FPGAs can be used for aggregating and accelerating data handling
at the edge of the IoT network.
Figure 3. Handling more of the IoT processing at the edge of the network
(Source: EdgeX Foundry)
